计算机视觉四大基本任务(分类、定位、检测、分割)
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2021-02-09 18:02
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转载于:作者 | 张皓 来源 | 知乎(https://zhuanlan.zhihu.com/p/31727402)
引言:深度学习目前已成为发展最快、最令人兴奋的机器学习领域之一,许多卓有建树的论文已经发表,而且已有很多高质量的开源深度学习框架可供使用。然而,论文通常非常简明扼要并假设读者已对深度学习有相当的理解,这使得初学者经常卡在一些概念的理解上,读论文似懂非懂,十分吃力。另一方面,即使有了简单易用的深度学习框架,如果对深度学习常见概念和基本思路不了解,面对现实任务时不知道如何设计、诊断、及调试网络,最终仍会束手无策。
图像分类(image classification)
目标定位(object localization)
目标检测(object detection)
语义分割(semantic segmentation)
实例分割(instance segmentation)
参考文献
V. Badrinarayanan, et al. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. TPAMI, 2017.
Y. Bengio, et al. Representation learning: A review and new perspectives. TPAMI, 2013.
L.-C. Chen, et al. SegNet: A deep convolutional encoder-decoder architecture for image segmentation. PAMI, 2017.
S. Chetlur, et al. cuDNN: Efficient primitives for deep learning. arXiv: 1410.0759, 2014.
J. Cong, and B. Xiao. Minimizing computation in convolutional neural networks. ICANN, 2014.
J. Dai, et al. R-FCN: Object detection via region-based fully convolutional networks. NIPS, 2016.
A. Garcia-Garcia, et al. A review on deep learning techniques applied to semantic segmentation. arXiv: 1704.06857, 2017.
R. Girshick, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. CVPR, 2014.
R. Girshick. Fast R-CNN. ICCV, 2015.
K. He, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition. ECCV, 2014.
K. He, et al. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. ICCV, 2015.
K. He, et al. Deep residual learning for image recognition. CVPR, 2016.
K. He, et al. Identity mappings in deep residual networks. ECCV, 2016.
K. He, et al. Mask R-CNN. ICCV, 2017.
J. Hu, et al. Squeeze-and-excitation networks. CVPR, 2018.
G. Huang, et al. Deep networks with stochastic depth. ECCV, 2016.
G. Huang, et al. Densely connected convolutional networks. CVPR, 2017.
J. Huang, et al. Speed/Accuracy trade-offs for modern convolutional object detectors. CVPR, 2017.
A. Krizhevsky, and G. Hinton. Learning multiple layers of features from tiny images. Technical Report, 2009.
A. Krizhevsky, et al. ImageNet classification with deep convolutional neural networks. NIPS, 2012.
A. Lavin, and S. Gray. Fast algorithms for convolutional neural networks. CVPR, 2016.
Y. LeCun, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
M. Lin, et al. Network in network. ICLR, 2014.
T.-Y. Lin, et al. Microsoft COCO: Common objects in context. ECCV, 2014.
T.-Y. Lin, et al. Feature pyramid networks for object detection. CVPR, 2017.
T.-Y. Lin, et al. Focal loss for dense object detection. ICCV, 2017.
W. Liu, et al. SSD: Single shot multibox detector. ECCV, 2016.
J. Long, et al. Fully convolutional networks for semantic segmentation. CVPR, 2015.
H. Noh, et al. Learning deconvolution network for semantic segmentation. ICCV, 2015.
G. Pleiss, et al. Memory-efficient implementation of DenseNets. arXiv: 1707.06990, 2017.
J. Redmon, et al. You only look once: Unified, real-time object detection. CVPR, 2016.
S. Ren, et al. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS, 2015.
S. Ren, et al. Object detection networks on convolutional feature maps. TPAMI, 2017.
O. Ronneberger, et al. U-net: Convolutional networks for biomedical image segmentation. MICCAI, 2015.
O. Russakovsky, et al. ImageNet large scale visual recognition challenge. IJCV, 2015.
P. Sermanet, et al. OverFeat: Integrated recognition, localization, and detection using convolutional networks. ICLR, 2014.
A. Shrivastava, et al. Training region-based object detectors with online hard example mining. CVPR, 2016.
K. Simonyan, and A. Zisserman. Very deep convolutional networks for large-scale image recognition. ICLR, 2015.
J. T. Springenberg, et al. Striving for simplicity: The all convolutional net. ICLR Workshop, 2015.
V. Sze, et al. Efficient processing of deep neural networks: A tutorial and survey. Proceedings of IEEE, 2017.
C. Szegedy, et al. Going deep with convolutions. CVPR, 2015.
C. Szegedy, et al. Rethinking the Inception architecture for computer vision. CVPR, 2016.
C. Szegedy, et al. Inception v4, Inception-ResNet and the impact of residual connections on learning. AAAI, 2017.
A. Toshev, and C. Szegedy. DeepPose: Human pose estimation via deep neural networks. CVPR, 2014.
A. Veit, et al. Residual networks behave like ensembles of relatively shallow networks. NIPS, 2016.
S. Xie, et al. Aggregated residual transformations for deep neural networks. CVPR, 2017.
F. Yu, and V. Koltun. Multi-scale context aggregation by dilated convolutions. ICLR, 2016.
M. D. Zeiler, and R. Fergus. Visualizing and understanding convolutional networks. ECCV, 2014.
S. Zheng, et al. Conditional random fields as recurrent neural networks. ICCV, 2015.
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